#rmarkdown::render(“ClusteringCoseq.Rmd”)
On cherche à construire un réseau de régulation, sur les gènes qui ont été détectés comme différentiellement exprimés lors du traitement au KNO3 des plantes WT.
Etude de la quantification
data <- read.csv("quantif_At.csv", h = T, sep = ",")
rownames(data) <- data$Gene
genes = which(!(grepl("__", rownames(data))))
data = data[genes, ]Voir les gènes non quantifiés aussi, combien, paraît beaucoup
Homogénéité des réplicats : visualisation
annot <- read.csv("Code_for_RNAseq_CO2_N_Fr.csv", h = T, sep = ";")
conditions <- as.vector(unique(annot$Sample))
annot$condition <- substr(conditions, 1, nchar(conditions) - 1)
cond <- unique(substr(conditions, 1, nchar(conditions) - 1))
cond <- cond[grepl("At", cond)]
cor_diff <- c()
cor_same <- c()
scatter <- function(df, x, y) {
sp <- ggplot(df, aes(log(df[, x]), log(df[, y]))) + geom_bin2d(bins = 70) + scale_fill_gradient2() +
theme(legend.position = "none") + labs(x = x, y = y)
return(sp)
}
for (co in cond) {
print(co)
samples <- paste0("R", annot[annot$condition == co, ]$Code)
df <- data[, samples]
a <- scatter(df, samples[1], samples[2])
b <- scatter(df, samples[1], samples[3])
c <- scatter(df, samples[2], samples[3])
grid.arrange(a, b, c, ncol = 3)
cor_same <- c(cor_same, cor(df[, samples[1]], df[, samples[2]]), cor(df[, samples[1]],
df[, samples[3]]), cor(df[, samples[2]], df[, samples[3]]))
other <- paste0("R", annot[annot$condition != co & !grepl("Sl", annot$Sample),
]$Code)
print(other)
for (o in other) {
print(data[, o])
cor_diff <- c(cor_diff, cor(data[, o], df[, samples[1]]))
}
}[1] "At_AmbientCO2_HighNitrate_Fe"
[1] "R4" "R5" "R6" "R7" "R8" "R9" "R10" "R11" "R12" "R13" "R14" "R15"
[13] "R16" "R17" "R18" "R19" "R20" "R21" "R22" "R23" "R24"
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[ reached getOption("max.print") -- omitted 40670 entries ]
[1] "At_AmbientCO2_LowNitrate_Fe"
[1] "R1" "R2" "R3" "R7" "R8" "R9" "R10" "R11" "R12" "R13" "R14" "R15"
[13] "R16" "R17" "R18" "R19" "R20" "R21" "R22" "R23" "R24"
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[ reached getOption("max.print") -- omitted 40670 entries ]
[1] "At_ElevatedCO2_HighNitrate_Fe"
[1] "R1" "R2" "R3" "R4" "R5" "R6" "R10" "R11" "R12" "R13" "R14" "R15"
[13] "R16" "R17" "R18" "R19" "R20" "R21" "R22" "R23" "R24"
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[ reached getOption("max.print") -- omitted 40670 entries ]
[1] "At_ElevatedCO2_LowNitrate_Fe"
[1] "R1" "R2" "R3" "R4" "R5" "R6" "R7" "R8" "R9" "R13" "R14" "R15"
[13] "R16" "R17" "R18" "R19" "R20" "R21" "R22" "R23" "R24"
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[ reached getOption("max.print") -- omitted 40670 entries ]
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[ reached getOption("max.print") -- omitted 40670 entries ]
[1] "At_AmbientCO2_HighNitrate_FeStarvation"
[1] "R1" "R2" "R3" "R4" "R5" "R6" "R7" "R8" "R9" "R10" "R11" "R12"
[13] "R16" "R17" "R18" "R19" "R20" "R21" "R22" "R23" "R24"
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[ reached getOption("max.print") -- omitted 40670 entries ]
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[ reached getOption("max.print") -- omitted 40670 entries ]
[1] "At_AmbientCO2_LowNitrate_FeStarvation"
[1] "R1" "R2" "R3" "R4" "R5" "R6" "R7" "R8" "R9" "R10" "R11" "R12"
[13] "R13" "R14" "R15" "R19" "R20" "R21" "R22" "R23" "R24"
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[ reached getOption("max.print") -- omitted 40670 entries ]
[1] "At_ElevatedCO2_HighNitrate_FeStarvation"
[1] "R1" "R2" "R3" "R4" "R5" "R6" "R7" "R8" "R9" "R10" "R11" "R12"
[13] "R13" "R14" "R15" "R16" "R17" "R18" "R22" "R23" "R24"
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[ reached getOption("max.print") -- omitted 40670 entries ]
[1] "At_ElevatedCO2_LowNitrate_FeStarvation"
[1] "R1" "R2" "R3" "R4" "R5" "R6" "R7" "R8" "R9" "R10" "R11" "R12"
[13] "R13" "R14" "R15" "R16" "R17" "R18" "R19" "R20" "R21"
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[ reached getOption("max.print") -- omitted 40670 entries ]Homogénéité des réplicats : significativité
On cherche maintenant à voir si ces plots montrent suffisamment corrélés pour montrer que les réplicats au sein d’une même condition sont suffisamment similaires. On peut déjà vérifier en plottant deux réplicats de conditions différentes : on voit que le scatter plot est plus dispersé que les réplicats biologiques.
cors = data.frame(`Different conditions` = cor_diff, `Same conditions` = cor_same)
library(reshape2)
cors <- melt(cors)
p <- ggplot(cors, aes(x = value, fill = variable)) + geom_density(alpha = 0.4) +
ggtitle("Distribution des corrélations entre les réplicats, biologiques ou non")
p